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SCRIPTPATH=$(dirname $(readlink -f "$0")) | |
PROJECT_DIR="${SCRIPTPATH}/../../" | |
# conda activate loftr | |
export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH | |
cd $PROJECT_DIR | |
data_cfg_path="configs/data/scannet_test_1500.py" | |
main_cfg_path="configs/model/indoor/model_cfg_test.py" | |
ckpt_path="pretrained/model_best.ckpt" | |
dump_dir="dump/loftr_ds_indoor" | |
profiler_name="inference" | |
n_nodes=1 # mannually keep this the same with --nodes | |
n_gpus_per_node=-1 | |
torch_num_workers=4 | |
batch_size=1 # per gpu | |
python -u ./test.py \ | |
${data_cfg_path} \ | |
${main_cfg_path} \ | |
--ckpt_path=${ckpt_path} \ | |
--dump_dir=${dump_dir} \ | |
--gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ | |
--batch_size=${batch_size} --num_workers=${torch_num_workers}\ | |
--profiler_name=${profiler_name} \ | |
--benchmark | |